Phase 4.5 intelligence layer
Phase 4.5 — Risks and Mitigations
Risks for fingerprinting, drift detection, regression testing, and rollout controls in the intelligence layer.
Phase 4.5 — Risks and Mitigations
| Risk | Area | Likelihood | Impact | Mitigation |
|---|---|---|---|---|
| Drift thresholds generate noisy alerts | 4.5.B.2 | Medium | High | Calibrate against replay data, keep thresholds in config, and revisit them as production evidence accumulates |
| Statistical tests do not generalize across agents | 4.5.B.x | Medium | Medium | Separate feature classes, document assumptions, and treat calibration as an iterative research task rather than a one-time constant choice |
| Fingerprint generation competes with hot-path workloads | 4.5.A.x | Low | High | Keep the whole pipeline asynchronous, time-box worker jobs, and benchmark fingerprint computation independently from the proxy |
| Judge-based regression results are unstable | 4.5.C.2 | Medium | Medium | Use repeated runs, track disagreement rate, and retain deterministic checks where they are sufficient |
| Promotion gate or rollout controls pause healthy agents incorrectly | 4.5.C.3 | Low | High | Start with shadow and notify-only modes, keep auto-actions opt-in, and require explicit rollback/pause verification in integration tests |
| Cross-tenant leakage in fingerprint or drift tables | 4.5.A.x + 4.5.B.x | Low | Critical | Apply the same org_id enforcement pattern used in earlier phases and keep dedicated cross-tenant tests in the exit suite |
| Dashboard narratives overstate drift confidence | 4.5.C.4 | Medium | Medium | Surface severity and confidence separately, show calibration provenance, and keep manual review available for critical decisions |
| Replay corpus is too narrow for calibration | 4.5.B.2 | Medium | Medium | Record the limitation explicitly, treat additional replay data as follow-up investigation work, and plan recalibration after broader coverage |
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